CN112464493B - Improved model based on TOPMODEL model, method for designing regional runoff and flood risk - Google Patents
Improved model based on TOPMODEL model, method for designing regional runoff and flood risk Download PDFInfo
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Abstract
An improved method based on a TOPMODEL model relates to the field of water circulation simulation and hydrologic design, solves the problems that the TOPMODEL model depends heavily on evaporation pan observation data, convergence calculation cannot reflect river network regulation and storage function and the like on the basis of the conventional TOPMODEL model by constructing mechanisms of potential evapotranspiration calculation, unit line earth surface convergence and the like with different data integration requirements, and improves the applicability and inversion accuracy of data-lacking area flow data inversion compared with the conventional TOPMODEL model. A method for designing regional runoff and flood risks is calculated by the improved method. Can provide powerful theoretical and technical support for designing section runoff and flood design and analysis decision in data-deficient areas.
Description
Technical Field
The invention relates to the field of water circulation simulation and hydrological design, in particular to a TOPMODEL model-based improved model and a regional runoff and flood risk design method.
Background
Compared with other watershed hydrological models, the TOPMODEL model has the remarkable characteristics due to the combination of the characteristics of a conceptual hydrological model and a distributed hydrological model: (1) based on a certain physical mechanism, but is structurally more refined and less complex; (2) the requirement on the data type is less, and the input data is easier to prepare; (3) the model parameters are few, and the parameter optimization is convenient; (4) the application is very wide, and the effect is generally tested well. Because of the above characteristics, the TOPMODEL is widely applied to relevant research and business such as basin hydrologic cycle simulation.
Although the inversion of the flow data of the data-lacking area belongs to the field of hydrologic cycle simulation research of a watershed, according to model theory and structural analysis, the TOPMODEL model applied to the inversion of the flow data of the data-lacking area at present has the following three defects: (1) in the area with data lack, the observation data of the evaporation pan is often lacked or even not existed, and the current model is seriously dependent on the observation data of the evaporation pan due to the lack of a potential evapotranspiration calculation mechanism, so that the model is difficult to apply. (2) And a snow melting runoff calculation mechanism is lacked, and the accuracy is not high when the flow of a high-cold high-altitude drainage basin is inverted. (3) The existing model confluence algorithm adopts an equal-flow time line method, cannot fully reflect the regulation and storage function of a drainage basin, cannot perform confluence calculation by dividing two water sources, namely surface water and underground water, and is low in confluence calculation precision. Due to the defects, the application effect of the TOPMODEL applied to the traffic data inversion research in the data-lacking area is poor, and even the TOPMODEL is difficult to apply.
From the perspective of longitudinal application, the most direct application of data inversion in data-deficient areas is to design runoff and flood of a designed section. With the development of professional technologies such as computers, remote sensing, hydrology and the like, at present, watershed hydrological models are applied more and more in the aspect, but relevant applications are all based on the concept of deterministic design, deterministic runoff and flood design results are obtained, risks caused by uncertainty of factors such as data, models and parameters of the results cannot be fully evaluated, and decision-making difficulty is brought to engineering design.
Disclosure of Invention
The invention aims to provide an improved model based on a TOPMODEL model, which is scientific and reasonable in design, optimizes the traditional TOPMODEL model and improves the applicability and the inversion accuracy of the flow data inversion of data-lacking areas.
Another objective of the present invention is to provide a method for designing regional runoff and flood risk, which can provide powerful theoretical and technical support for designing cross-sectional runoff and flood design and analysis decision in data-deficient areas based on the improved model.
The embodiment of the invention is realized by the following steps:
an improved model based on the TOPMODEL model, comprising:
and matching a corresponding potential evapotranspiration calculation formula according to the acquired environmental parameters, calculating the potential evapotranspiration, and applying the potential evapotranspiration to the TOPMODEL model.
A method for designing regional runoff and flood risks adopts the improved model calculation.
The embodiment of the invention has the beneficial effects that:
the embodiment of the invention provides an improved model based on a TOPMODEL model, which overcomes the problem that the TOPMODEL model depends heavily on evaporation vessel observation data by constructing a potential evapotranspiration computing mechanism integrating different data requirements on the basis of the current TOPMODEL model, and improves the applicability and inversion accuracy of data-lacking area flow data inversion compared with the current TOPMODEL model.
The embodiment of the invention also provides a method for designing regional runoff and flood risks, which adopts the improved model for calculation. Can provide powerful theoretical and technical support for designing section runoff and flood design and analysis decision in data-deficient areas.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic diagram of a TOPMODEL-based improved model and a method for designing regional runoff and flood risk according to an embodiment of the present invention;
fig. 2 is a frequency analysis (review) of the annual runoff of a station in a menthyl hydrology according to an embodiment of the present invention;
fig. 3 is a mengwei hydrology station year runoff frequency analysis (optimal parameter calculated flow) provided by an embodiment of the invention;
FIG. 4 is a Monwey hydrology standing runoff frequency analysis (33.3% lower bound flow) provided by an embodiment of the present invention;
FIG. 5 is a Monwey hydrology station year runoff frequency analysis (weighted flow) provided by an embodiment of the present invention;
fig. 6 is a montwey hydrology standing peak frequency analysis (review examination) provided by an embodiment of the present invention;
fig. 7 is a frequency analysis of annual peak flood of the mengweis station (optimal parameter calculated flow rate) provided by an embodiment of the present invention;
fig. 8 is a monte hydrology station annual peak frequency analysis (upper bound flow) provided by an embodiment of the present invention;
fig. 9 is a weighted flow rate analysis of annual peak frequencies of the mengweis station provided by an embodiment of the present invention.
Detailed Description
The following describes an improved model based on a TOPMODEL and a method for designing regional runoff and flood risk in an embodiment of the present invention.
A TOPMODEL-based improved model, comprising:
and matching a corresponding potential evapotranspiration calculation formula according to the acquired environmental parameters, calculating the potential evapotranspiration, and applying the potential evapotranspiration to the TOPMODEL model.
In the prior art, evaporation vessel observation data is often lacked or even not available in data-deficient areas, and the existing TOPMODEL model is seriously dependent on the evaporation vessel observation data due to the lack of a potential evaporation calculation mechanism, so that the model is difficult to apply. Aiming at the problems, the embodiment of the invention selects the existing formula for calculating the evapotranspiration amount and collects the formula into a database. And matching a proper calculation formula according to the acquired environmental parameters, thereby solving the application problem of the TOPMODEL model in the data-lacking area.
Further, when the environmental parameters that can be obtained include the daily average air temperature, the daily maximum air temperature, and the daily minimum air temperature, the potential evapotranspiration amount is calculated using the formula of Hargreaves (1985).
If A is less than 0.75, the potential evapotranspiration amount is calculated according to the formula
If A is more than or equal to 0.75, the potential evapotranspiration amount is calculated according to the formula
In the formula, ET is the potential evapotranspiration, a = Krs 0.0135 (T) max -T min ) 0.5 The Krs is 0.16-0.19, and can be 0.16 when the area is inland and 0.19 when the area is coastal. T is max The highest daily temperature, T min The daily minimum temperature, T a The average daily temperature.
Further, when the environmental parameters which can be obtained are increased by the average day sunshine hours in addition to the average day air temperature, the potential evapotranspiration amount calculation formula is replaced by a Hargreaves (1975) formula,
in the formula, lambda is latent heat of evaporation and can be obtained by calculation according to the daily average air temperature; r s The short-wave radiant quantity can be obtained by calculation according to the average day sunshine hours, the point position elevation and the latitude; other parameters are the same as Hargreaves (1985).
Further, when the average daily relative humidity is increased in addition to the average daily temperature and the average daily sunshine hours of the environmental parameters which can be obtained, the potential evapotranspiration calculation formula is replaced by a Turc formula.
If the RH is more than or equal to 50, the potential evapotranspiration calculation formula is
If RH is less than 50, the calculation formula of the potential evapotranspiration amount is
Wherein RH is the average daily relative humidity, and other parameters are the same as Hargreaves (1975) formula.
Further, when the environmental parameters which can be obtained are increased in the daily average wind speed in addition to the daily average air temperature, the daily average relative humidity and the daily average sunshine hours (or the daily average incoming short-wave radiation quantity), the potential evapotranspiration quantity calculation formula is replaced by a Penman-Monteith formula,
in the formula, delta is the corresponding slope of a saturated water air pressure-temperature curve and can be obtained by calculation according to the daily average air temperature; g is the soil heat flux density, and can generally take the value of 0; gamma is a hygrometer constant and can be obtained by calculation according to the elevation of the point position; u shape 2 The wind speed at the height of 2m can be obtained by calculation according to the point position elevation and the daily average wind speed; e.g. of a cylinder s The saturated water vapor pressure can be obtained indirectly according to the daily average air temperature; e.g. of the type a The actual water vapor pressure can be indirectly determined according to the dayCalculating the average temperature and the daily average relative humidity; r n The net radiant quantity can be calculated according to the daily average sunshine hours (or daily average incoming short wave radiant quantity), the point position elevation, the latitude and the daily average air temperature.
The formula is selected in a progressive mode, and the formula which is more suitable and has better calculation precision can be automatically matched every time partial environment parameters are added, so that the accuracy of the result is improved.
In addition, the improved model based on the TOPMODEL provided by the embodiment of the present invention further includes:
introducing a snow-melting runoff calculation formula to simulate the snow accumulation and snow melting process, and processing the original rainfall input to obtain a clean rain process; introducing the calculation result into a TOPMODEL model; wherein, the calculation formula of the snow melting runoff is as follows,
M s ＝C S (T a -T t )， (7)
in the formula, M s A positive value indicating the amount of snow melting, a negative value indicating the amount of snow accumulation, C s Is a weekday factor, T t Is the critical air temperature, T a The average daily temperature.
If the rainfall in the time period is P and the snow accumulation depth in the early period is S, calculating the snow accumulation depth at the end of the time period to be S-M s (not less than 0) and the time interval of net rain is P + M s (M s <S) or P + S (M) s ≥S)。
The existing TOPMODEL model is lack of a snow-melting runoff calculation mechanism, and the accuracy is not high when the flow of a high-cold high-altitude drainage basin is inverted. The embodiment of the invention is more suitable for the calculation of the snowmelt runoff in alpine and high-altitude areas and areas lacking data under the condition of only adding one degree-day factor parameter, and further improves the calculation precision.
Further, an improved model based on the TOPMODEL provided by the embodiment of the present invention further includes:
and replacing the equal-flow time line convergence algorithm of the TOPMODEL model by adopting a Nash geomorphy unit line surface convergence algorithm and a linear reservoir underground convergence algorithm.
(1) The earth surface confluence part adopts an earth surface confluence algorithm of a Nash landform unit line, and the Nash landform unit line has the following basic form:
in the formula: n is a parameter reflecting the watershed storage regulation capacity, is equivalent to the number of linear reservoirs or the regulation times of the reservoirs, and K is the storage and discharge coefficient of the linear reservoirs and has a time factor; Γ (n) is a function of Γ, i.e.
And calculating surface runoff in a certain period of time in the future according to the surface runoff yield in each calculation period by the convolution equation, and superposing the surface runoff processes in each period to obtain the final drainage basin outlet surface runoff process.
In the calculation of the parameter n and the parameter K, the geometric rate (area ratio, river length ratio and bifurcation ratio) of the Howden landform can be calculated as follows:
in the formula: r B ，R L ，R A The bifurcation ratio, the river length ratio and the area ratio of the watershed water system can be determined by DEM data based on the Stellarer scale.
The problem of estimating the K parameter is how to determine the average sink time of the basin based on the topographic data. According to the fact that the flow rate of rivers with different levels mainly depends on the terrain gradient, the following relation is provided:
τ＝1-(1-λ)(1-ρ) (10)
wherein:
further analysis can also yield the following relationships:
τ＝λ 1-mλ (12)
from equations (10) and (11) it can also be deduced:
using the hodton river length law, one can deduce:
in the above formula: tau is the ratio of the average confluence time of the net rain particles from the river source to a certain section of the downstream to the average confluence time of the river source to the section of the drainage basin outlet; rho is a parameter related to the river length and the river bottom drop; n is the number of sub-river sections from the river source to a certain section at the downstream; n is the number of sub-river sections from a river source to the cross section of an outlet of a river basin; Δ l j The length of the jth sub-river section divided from the river source; p is a radical of j The average slope of the jth sub-river segment is obtained; m is a comprehensive parameter reflecting the longitudinal section characteristics of the river channel; omega is the stage number of the highest-level river of the river system; v Ω The flow velocity of the outlet section of the drainage basin is generally given by the average flow velocity of the flood rise section of the outlet section flood process line; alpha is the ratio of the distance from the center of the basin to the cross section of the outlet of the basin to the length of the basin.
The method is innovative in the aspect of m parameter calculation, the m parameter can be considered as a comprehensive parameter reflecting the characteristics of the longitudinal section of the river channel, at present, tau and lambda are calculated firstly by combining actual data of the main and branch streams, and tau-lambda graphs are plotted for analysis, so that the method is complex.
The invention provides a river length reduction ratio R according to a Hotten river length law and a reduction law and aiming at rho parameter calculation LS This concept, which is the river lp at each level -0.5 The average ratio of the values is:
on the basis, the parameter m can be conveniently calculated by combining the formula (10) and the formula (12) through iterative solution.
(2) The underground confluence part adopts a linear reservoir algorithm, and the basic form of the linear reservoir is as follows:
because the water surface of the underground water is relatively flat and smooth, the rising and falling water storage and discharge relations of the underground water are the same, and the calculation formula of the linear reservoir is as follows:
Q g2 ＝R g (1-CG)U+Q g1 CG (16)
in the formula: r is g Is the basal flow depth; CG is groundwater regression coefficient; q g1 、Q g2 Respectively the base flow of the previous time interval and the current time interval; u is a unit conversion coefficient.
And (3) iteratively calculating the underground runoff rate according to the surface runoff rate and the underground runoff rate in each calculation time period by adopting an equation (16).
The equi-current time line algorithm with defects in theory and structure is replaced by the comprehensive Nash geomorphy unit line earth surface convergence algorithm and the linear reservoir underground convergence algorithm.
A method for designing regional runoff and flood risks adopts the improved model calculation.
Preferably, the method adopts a GLUE algorithm and an improved model for coupling calculation.
Further, it comprises:
randomly generating a plurality of parameter groups by adopting a Monte Carlo algorithm, respectively substituting the parameter groups into the improved model, calculating a simulated flow process, calculating to obtain a corresponding likelihood function value by combining with an actual flow process, further calculating the weight of each parameter group, and calculating to obtain an upper boundary flow process, a lower boundary flow process, a weighted flow process and an optimal parameter flow process of uncertainty of corresponding confidence by combining with a set confidence index according to weight accumulation and flow sequencing;
calculating to obtain long series of annual and monthly runoff corresponding to different flow processes based on a lower boundary flow process, a weighted flow process and a theoretical optimal flow process; calculating to obtain long series of annual and monthly flood peaks corresponding to different flow processes based on the upper boundary flow process, the weighted flow process and the optimal parameter flow process; and substituting the runoff and flood peak results of each long series of years and months and the flood peak results of each long series of years and months into a frequency calculation method to obtain corresponding runoff and flood calculation results of different series.
Further, coupled with the global likelihood uncertainty analysis algorithm for the GLUE, the relevant documents of the algorithm have already been described, and the description is omitted. On the basis of the basic principle of the GLUE algorithm, the invention has certain characteristic setting for accelerating the calculation efficiency, and the following is introduced:
and randomly generating thousands of sets of parameter sets by adopting a Monte Carlo random sampling mode, carrying out parameter evaluation on the improved model, and screening to obtain a plurality of effective parameter sets on the basis of certain likelihood function threshold setting. The weight of each effective parameter group is:
in the formula: weight i Weights for the ith set of valid parameter sets; l is a radical of an alcohol i Likelihood values for the ith set of valid parameter sets; n is the total number of groups of the effective parameter group.
On the basis, for each calculation time interval, sorting the flow values by adopting a bubbling algorithm, and performing cumulative calculation according to a certain confidence coefficient Z and the weight value, taking the selection of the upper flow boundary as an example:
if accumulated, we get:
in the formula: a is an accumulation sequence number according to a certain sequence.
Then Q is a I.e. the upper bound traffic for the corresponding calculation period.
Similarly, the lower boundary of the flow is selected, which is similar to the upper boundary of the selected flow according to the principle, and is just opposite to the weight value accumulation direction.
Compared with the currently adopted deterministic method, the coupled GLUE universal likelihood uncertainty analysis algorithm provided by the invention can obtain the upper and lower uncertainty boundaries of the flow process under a certain confidence coefficient, can obtain the design result of partial acceleration or partial conservation under the corresponding confidence coefficients of runoff and flood by combining frequency analysis, and can provide powerful theoretical and technical support for designing section runoff and flood design and analysis decision in data-deficient areas.
The features and properties of the present invention are described in further detail below with reference to examples.
Examples
The method comprises the following steps: the Monty station selected for inversion of climate modes is researched and selected to control daily average air temperature, daily average actual water vapor pressure, daily average wind speed and daily average ground short wave radiation quantity of a basin in 1994-2002, the daily average air temperature, the daily average actual water vapor pressure, the daily average wind speed and the daily average ground short wave radiation quantity are substituted into a potential evapotranspiration calculation mechanism integrating different data requirements, and the potential evapotranspiration quantity of the basin is calculated and used as the evapotranspiration quantity input of an area lacking data.
Step two: based on the existing (unmodified) TOPMODEL model, a runoff generating calculation mechanism is kept unchanged, the original constant flow time line mechanism is replaced by an improved mechanism which adopts a Nash landform unit line surface convergence algorithm to calculate the surface runoff and adopts a linear reservoir underground convergence algorithm to calculate the underground runoff, a snowmelt runoff calculation mechanism based on a holiday factor is added, the snowmelt production flow is calculated time by time after the model operates, and precipitation input is corrected. Through the above improvement, an improved model is obtained.
Step three: and setting parameter ranges. Different from the empirical setting of the parameter range of the current TOPMODEL model, the parameter range setting of the improved model is more scientific and reasonable: the parameter n is obtained by DEM data according to the formula (2) based on the Sterler level; the parameter K is related to the average flow velocity of the flood section of the outlet section, and the value range of the parameter K can be further determined according to the flow velocity range determined by the section survey data; the linear reservoir parameters can be obtained by directly analyzing actual measurement flow data of a rainfall-free recession section; the degree day factor parameter can be drawn up in an empirical way according to the type of the climate zone; other parameter range determination methods are consistent with the current TOPMODEL.
Step four: the GLUE algorithm is coupled with the improved model. Based on the parameter range result of the third step, a plurality of (generally more than 5000 times) parameter sets are randomly generated by adopting a Monte Carlo algorithm, the parameter sets are respectively substituted into an improved model to calculate a simulated flow process, corresponding likelihood function values (the likelihood function is optional and can be defaulted to be a Nash efficiency coefficient) are calculated by combining with an actual flow process, then the weight of each parameter set is calculated, and an upper boundary flow process, a lower boundary flow process, a weighted flow process (obtained by weighting according to the weight of the parameter sets) and an optimal parameter flow process (corresponding to the parameter set calculation result with the maximum likelihood function value) of corresponding confidence coefficients are calculated by combining with artificially set confidence indexes according to weight accumulation and flow sequencing. In the implementation process of the step, the GLUE algorithm repeatedly calls the improved model.
Step five: calculating to obtain long series of annual and monthly runoff corresponding to different flow processes based on the lower boundary flow process (the confidence coefficient can be generally 33.3 percent), the weighted flow process and the theoretical optimal flow process obtained in the step four; and calculating to obtain long series of yearly and monthly flood peaks corresponding to different flow processes based on the upper boundary flow process (the confidence coefficient can be generally 90 percent), the weighted flow process and the optimal parameter flow process obtained in the step four. And substituting the runoff and flood peak results of each long series of years and months and the flood peak results of each long series of years and months into a frequency calculation method to obtain corresponding runoff and flood calculation results of different series.
The results of the embodiment are shown in FIGS. 2 to 9 and tables 1 to 2. It can be seen that:
(1) in runoff design, the frequency calculation results of the year runoff obtained in the optimal parameter flow process and the weighting flow process are closer to the overall evaluation result, and the frequency calculation results of the year runoff obtained in the lower boundary flow result with the confidence level of 33.3% are smaller than the frequency calculation results of the year runoff obtained in the lower boundary flow result with the confidence level of 33.3% except for a slightly larger dead period, so that the frequency calculation results of the year runoff obtained in the lower boundary flow result with the confidence level of 33.3% can be used as the control results of the smaller runoff. Comprehensive analysis shows that the method can obtain runoff partial homogenization and partial conservation risk design results, and can better serve decision makers compared with the deterministic runoff design results.
(2) In terms of flood design, the annual peak frequency calculation result obtained in the optimal parameter flow process is closer to the evaluation result in general, the annual peak frequency calculation result obtained in the boundary flow process at the confidence level of 90% is larger than the evaluation result in general, and the annual peak frequency calculation result obtained in the weighted flow process is smaller than the evaluation result in general. Comprehensive analysis shows that the risk design results of flood partial conservation and partial homogenization (aggressiveness) obtained by the method are combined with the frequency calculation results obtained by calculating the flow rate by the optimal parameters, so that various choices are provided, and compared with the deterministic flood design results, a decision maker can make a risk decision according to specific consideration.
TABLE 1 Monty standing year runoff frequency calculation result table obtained by calculation according to the invention
| Method | Mean value | Cv | Cs/Cv | P＝10％ | P＝50％ | P＝90％ |
| Review and |
436 | 0.26 | 2 | 586 | 426 | 299 |
| Optimal |
458 | 0.20 | 2 | 579 | 452 | 345 |
| |
485 | 0.19 | 2 | 606 | 479 | 371 |
| Lower boundary of 33 |
414 | 0.21 | 2 | 529 | 408 | 307 |
Table 2 Monty station annual peak frequency calculation result table obtained by calculation according to the present invention
| Method | Mean value | Cv | Cs/Cv | P＝0.01％ | P＝0.02％ | P＝0.05％ | P＝0.1％ | P＝1％ | P＝5％ |
| Review evaluation (Standard) | 3690 | 0.56 | 3.5 | 20500 | 19100 | 17200 | 15800 | 11000 | 7810 |
| Optimal |
3550 | 0.59 | 3.5 | 21100 | 19600 | 17600 | 16100 | 11200 | 7750 |
| Upper bound |
6500 | 0.36 | 5 | 24700 | 23200 | 21200 | 19700 | 14700 | 11100 |
| |
3810 | 0.5 | 3 | 17300 | 16300 | 14900 | 13800 | 10200 | 7530 |
In summary, the embodiment of the present invention provides an improved model based on a topmode model, which makes up the problem that the topmode model depends heavily on the observation data of the evaporation pan by constructing a potential evapotranspiration calculation mechanism with different data integration requirements on the basis of the current topmode model, and improves the applicability and inversion accuracy of data-lacking area traffic data inversion compared with the current topmode model.
The embodiment of the invention also provides a method for designing regional runoff and flood risks, which adopts the improved model for calculation. Can provide powerful theoretical and technical support for designing section runoff and flood design and analysis decision in data-deficient areas.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A TOPMODEL model-based improvement method is characterized by comprising the following steps:
according to the collected environmental parameters, matching a corresponding potential evapotranspiration calculation formula, calculating potential evapotranspiration, and applying the potential evapotranspiration to the TOPMODEL model;
the environmental parameters comprise daily average air temperature, daily maximum air temperature and daily minimum air temperature, the potential evapotranspiration amount is calculated by the formula,
or
（A≥0.75）；
In the formula (I), the compound is shown in the specification,ETin order to be a potential amount of the evapotranspiration,
，Krsis 0.16 to 0.19, in weight percent,T max the temperature is the highest temperature of the day,T min the temperature is the lowest temperature in the day,T a the average daily air temperature is the average daily air temperature,
for evaporating latent heat;
if the environmental parameters comprise the daily average air temperature and the daily average sunshine hours, the potential evapotranspiration calculation formula is replaced by,
in the formula (I), the compound is shown in the specification,
for evaporating latent heat;R s short-wave radiation dose;
if the environmental parameters comprise daily average air temperature, daily average relative humidity and daily average sunshine hours, replacing the potential evapotranspiration calculation formula with a formula,
or
（RH＜50）；
In the formula (I), the compound is shown in the specification,RHthe average daily relative humidity;
if the environmental parameters comprise daily average air temperature, daily average relative humidity, daily average wind speed and daily average sunshine hours, replacing the potential evapotranspiration calculation formula with a formula,
in the formula (I), the compound is shown in the specification,
the slope is corresponding to a saturated water air pressure-temperature curve;Gis the soil heat flux density;
is the hygrometer constant;U 2 the wind speed at the height of 2 m;e s saturated water vapor pressure;e a the actual water vapor pressure;R n is the net dose;
the improved method further comprises:
introducing a snow-melting runoff calculation formula to simulate the snow accumulation and snow melting process, and processing the original rainfall input to obtain a clean rain process; introducing the calculation result into the TOPMODEL model; wherein the calculation formula of the snow melting runoff is as follows,
in the formula (I), the compound is shown in the specification,M s in order to melt or accumulate snow,C s in order to be the factors of the degree day,T t is the critical air temperature, and the air conditioner is controlled,T a the average daily temperature;
the improved method further comprises:
and replacing the equal-flow time line convergence algorithm of the TOPMODEL model by adopting a Nash geomorphy unit line surface convergence algorithm and a linear reservoir underground convergence algorithm.
2. A method for designing runoff and flood risks in an area, characterized in that the improved method of claim 1 is used in combination with a GLUE algorithm for calculation.
3. A method of regional runoff and flood risk design according to claim 2 including:
randomly generating a plurality of parameter groups by adopting a Monte Carlo algorithm, respectively substituting the parameter groups into the improvement method, calculating a simulated flow process, calculating to obtain a corresponding likelihood function value by combining with an actual flow process, further calculating the weight of each parameter group, and calculating to obtain an upper boundary flow process, a lower boundary flow process, a weighted flow process and an optimal parameter flow process of uncertainty of corresponding confidence by combining with a set confidence index according to weight accumulation and flow sequencing;
calculating to obtain long series of annual runoff and monthly runoff corresponding to different flow processes based on the lower boundary flow process, the weighted flow process and the optimal parameter flow process; calculating to obtain long series of yearly and monthly flood peaks corresponding to different flow processes based on the upper boundary flow process, the weighted flow process and the optimal parameter flow process; and substituting the runoff and flood peak results of each long series of years and months into a frequency calculation method to obtain corresponding runoff and flood calculation results of different series.
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